{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ba8adbdc",
   "metadata": {},
   "source": [
    "# 用Seaborn实现分布的可视化\n",
    "Seaborn是一个使用Matplotlib在下面绘制图形的库。它将被用来对随机分布进行可视化。\n",
    "## 安装Seaborn。\n",
    "如果你已经在系统上安装了Python和PIP，请用这个命令来安装它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d2c83e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: http://pypi.douban.com/simple\n",
      "Collecting seaborn\n",
      "  Downloading http://pypi.doubanio.com/packages/68/ad/6c2406ae175f59ec616714e408979b674fe27b9587f79d59a528ddfbcd5b/seaborn-0.11.1-py3-none-any.whl (285 kB)\n",
      "Collecting scipy>=1.0\n",
      "  Downloading http://pypi.doubanio.com/packages/45/42/fc32b88f0dd6e9dad2850766b7a35024ee5df6a99c5f0836f9d5455d1fde/scipy-1.7.0-cp37-cp37m-win_amd64.whl (33.6 MB)\n",
      "Requirement already satisfied: matplotlib>=2.2 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from seaborn) (3.4.2)\n",
      "Requirement already satisfied: numpy>=1.15 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from seaborn) (1.20.3)\n",
      "Requirement already satisfied: pandas>=0.23 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from seaborn) (1.2.4)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib>=2.2->seaborn) (8.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: six in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.1)\n",
      "Installing collected packages: scipy, seaborn\n",
      "Successfully installed scipy-1.7.0 seaborn-0.11.1\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0537cd",
   "metadata": {},
   "source": [
    "## Matplotlib 的安装\n",
    "如果您的系统上已经安装了 Python 和 PIP，那么安装 Matplotlib 非常容易。\n",
    "\n",
    "使用以下命令安装它："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37264bcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: http://pypi.douban.com/simple\n",
      "Requirement already satisfied: matplotlib in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (3.4.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: numpy>=1.16 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib) (1.20.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib) (8.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: six in d:\\anaconda3\\envs\\pandascourse\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12d800b",
   "metadata": {},
   "source": [
    "# 分布图\n",
    "Distplot是分布图的意思，它将一个数组作为输入，并绘制出与数组中的点的分布相对应的曲线。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b56b61",
   "metadata": {},
   "source": [
    "## 导入\n",
    "在你的代码中使用以下语句导入Matplotlib模块的pyplot对象和Seaborn模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e372816f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e21e5f42",
   "metadata": {},
   "source": [
    "## 绘制图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "010e61fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot([0, 1, 2, 3, 4, 5])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2260f1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot([0, 1, 2, 3, 4, 5])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
